When LLMs Enter Everyday Feminism on Chinese Social Media: Opportunities and Risks for Women's Empowerment

TL;DR

This paper examines how large language models influence everyday feminist discussions on Chinese social media, highlighting opportunities for empowerment and risks of reinforcing normative gender roles.

Contribution

It provides an empirical analysis of LLM interactions within Chinese feminist online spaces, revealing their tendency to promote normative self-optimization over challenging norms.

Findings

Users generally welcomed DeepSeek responses

Responses mainly encouraged women to self-optimize within norms

Risks include reinforcing gender biases and norms

Abstract

Everyday digital feminism refers to the ordinary, often pragmatic ways women articulate lived experiences and cultivate solidarity in online spaces. In China, such practices flourish on RedNote through discussions under hashtags like ''women's growth''. Recently, DeepSeek-generated content has been taken up as a new voice in these conversations. Given widely recognized gender biases in LLMs, this raises critical concerns about how LLMs interact with everyday feminist practices. Through an analysis of 430 RedNote posts, 139 shared DeepSeek responses, and 3211 comments, we found that users predominantly welcomed DeepSeek's advice. Yet feminist critical discourse analysis revealed that these responses primarily encouraged women to self-optimize and pursue achievements within prevailing norms rather than challenge them. By interpreting this case, we discuss the opportunities and risks that LLMs introduce for everyday feminism as a pathway toward women's empowerment, and offer design implications for leveraging LLMs to better support such practices.